1. Field of the Invention
The invention relates generally to a method and system for the computerized automatic analysis of lesions in ultrasound images. Specifically the system includes the computerized analysis of lesions in the breast using gradient, gray-level, and texture based measures. Techniques include novel developments and implementations of echogenicity features to assess the characteristics of the lesions and in some cases give an estimate of the likelihood of malignancy or of prognosis. Output from the computerized analysis can related to and used in the diagnostic decision making process.
The present invention generally relates to CAD techniques for automated detection of abnormalities in digital images, for example as disclosed in one or more of U.S. Pat. Nos. 4,839,807; 4,841,555; 4,851,984; 4,875,165; 4,907,156; 4,918,534; 5,072,384; 5,133,020; 5,150,292; 5,224,177; 5,289,374; 5,319,549; 5,343,390; 5,359,513; 5,452,367; 5,463,548; 5,491,627; 5,537,485; 5,598,481; 5,622,171; 5,638,458; 5,657,362; 5,666,434; 5,673,332; 5,668,888; and 5,740,268; as well as U.S. patent applications Ser. Nos. 08/158,388; 08/173,935; 08/220,917; 08/398,307; 08/428,867; 08/523,210; 08/536,149; 08/536,450; 08/515,798; 08/562,087; 08/757,611; 08/758,438; 08/900,191; 08/900,188; 08/900,189; and 08/900,192. The present invention includes use of technologies referenced and described therein, as well as described in the references identified in the appended APPENDIX and cross-referenced throughout the specification by reference to the number, in brackets, of the respective reference listed in the APPENDIX, the entire contents of which, including the related patents and applications listed above and references listed in the APPENDIX, are incorporated herein by reference.
2. Discussion of the Background
Breast cancer is a leading cause of death in women, causing an estimated 46,000 deaths per year [1]. Mammography is the most effective method for the early detection of breast cancer, and it has been shown that periodic screening of asymptomatic women does reduce mortality [2-4]. Various medical organizations have recommended the use of mammographic screening for the early detection of breast cancer. Thus, mammography is becoming one of the largest volume x-ray procedures routinely interpreted by radiologists. Many breast cancers are detected and referred for surgical biopsy on the basis of a radiographically detected mass lesion or cluster of microcalcifications. Although general rules for the differentiation between benign and malignant mammographically identified breast lesions exist [5, 6], considerable misclassification of lesions occurs with the current methods. On average, less than 30% of masses referred for surgical breast biopsy are actually malignant [7]. A computerized method capable of detecting and analyzing the characteristics of benign and malignant masses, in an objective manner, can aid radiologists by reducing the numbers of false-positive diagnoses of malignancies, thereby decreasing patient morbidity as well as the number of surgical biopsies performed and their associated complications [8].
Breast sonography is used as an important adjunct to diagnostic mammography and is typically performed to evaluate palpable and mammographically identified masses in order to determine their cystic vs. solid natures. The accuracy of ultrasound has been reported to be 96-100% in the diagnosis of simple benign cysts [9]. Masses so characterized do not require further evaluation; however, 75% of masses prove to be indeterminate or solid on sonography and are candidates for further intervention [10]. Ultrasound has not been used for screening purposes due to relatively high false-negative and false-positive rates [11]. Also, breast sonography has not been routinely used to distinguish benign from malignant solid masses because of the considerable overlap in their sonographic appearances [12]. With the advent of modem high-frequency transducers that have improved spatial and contrast resolution, a number of sonographic features have emerged as potential indicators of malignancy, while other features are typical for benign masses [13, 14]. Benign features include hyperechogenicity, ellipsoid shape, mild lobulation, and a thin, echogenic pseudocapsule. Malignant features include spiculation, angular margins, marked hypoechogenicity, posterior acoustic shadowing, and depth:width ratio greater than 0.8. Recently, Stavros, et al., used these and other features to characterize masses as benign, indeterminate, and malignant [15]. Their classification scheme had a sensitivity of 98.4% and a negative predictive value of 99.5%. However, the sonographic evaluation described by these investigators is much more extensive and complex than is usually performed at most breast imaging centers. Ultrasound examination is a notoriously operator-dependent modality, and until these encouraging results are corroborated through additional studies by other investigators, it is unclear how widely applicable this sonographic classification scheme will be.
An automated technique that can objectively and reliably classify lesions based upon reported sonographic characteristics of benign and malignant masses, especially if combined with their mammographic features, could significantly improve the specificity of breast imaging evaluation of breast masses. Computer-aided techniques have been applied to the color Doppler evaluation of breast masses with promising results [16]. However, color Doppler imaging is a technique which focuses only upon the vascularity of lesions. Since not all sonographically visible cancers have demonstrable neovascularity, this technique is inherently somewhat limited. On the other hand, computer-aided diagnosis techniques applied to gray-scale sonographic images has not yet been reported.